Model-Based Diagnostic Decision-Support System for Satellites

Alexander Feldman, Helena Vicente de Castro,Arjan van Gemund, and Gregory ProvanMar 04, 2013 @ 09:45, in Lake/CanyonAEROCONF-2013, Big Sky, Montana

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Introduction

• History– LYDIA – diagnosis of Boolean circuits

• SAFARI – stochastic diagnosis• FRACTAL – disambiguation of diagnoses (reduce diagnostic uncertainty – entropy

based methods)• Beyond diagnosis – worst case sensor data – diagnosability (MIRANDA)

– A resistor network can be modeled with Boolean variables but that is very difficult, e.g., 4-bit multiplication tables

– LYDIA-NG – generalization to continuous variables• SAFARI-NG – greedy stochastic reasoning, but also other candidate generation policies• FRACTAL-NG – disambiguation

• The insight that allows generalization of Lydia to Lydia-NG is that simulation is a key-component in model-based diagnosis– What is the simulation step in the MBD circuit problem shown in the next

slide?

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GOCE

4

GOCE (cont.)

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GENIUS Overview

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SCOS-2000 Integration

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GOCE Electrical Power System

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Fault Diagnosis

• Identify component(s) that are root cause of failure (error)

x, y: observation vectorsf: system function, fi: component functions

h: system health state vector, hi: component health variables

• Diagnose failure: solve inverse problem h = f-1(x,y)• Diagnosis: h2 = fault state, or h4 and h5 = fault state

y = f(x,h)xf1 f2

f4 f5

f3 healthy

faulty

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LYDIA-NG - Introduction

• LYDIA-NG is a diagnostic framework– Use cases:

• modeling of diagnostic systems (also an IDE)• running of diagnostic scenarios in batch mode (computation of

diagnostic metrics)• embedding in a SCADA system, e.g., a BMS

• Our view on the development of LYDIA-NG– collection of tools (simulators, translators, etc.)– simple interfaces (our users are not assumed to be expert

MBD users)– higher coding/documentation/knowledge dissemination

standards than typical projects

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LYDIA-NG Overview

• Core libraries– simulation

• SPICE• symbolic• ODEs/DAEs• Boolean circuits (old LYDIA heritage)

– diagnosis• forward reasoning (simulation for various health/fault states)• backward reasoning (residual analysis)

– disambiguation• entropy-based selection of tests• virtual sensors (by-product)

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State of LYDIA-NG Development

• LYDIA-NG has reached a milestone in diagnosing an electrical system– results show that the

software will be useful in practice

– releasing version 1.0 after fixing some bugs, documentation, and testing

– preview versions of the software continuously made available to the community for purpose of progress-tracking, collaboration, and planning

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LYDIA-NG Approach to Diagnosis

• The main idea is to run multiple-simulations simultaneously (each simulation reflects different health state)

• Choose those simulations that minimize some residual function

• Report diagnosis as a probability of each component being healthy/faulty

Model

Generate diagnostic

assumptions health1 health2 healthn

Simulate Simulate ...

Simulate

prediction1 prediction2 predictionn

Sensor

readings

Analyze residual

Analyze residual

... Analyze residual

candidate1 candidate2 candidaten

Choose plausible

candidates

set of plausible candidates

Compute probability for each component state

(healthy, faulty)

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GOCE Simulation

• Why SPICE– this is how electrical engineers simulate circuits

• very precise• fast• bug-free

• Caveats– going too detailed increases accuracy but this

takes computational time, it is too error-prone, time consuming (modeling effort), and is not needed for diagnosis (it can actually harm)

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Modified Nodal Analysis

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Fault Modeling

• Current sensor stuck-at OOB value• Voltage sensor stuck-at OOB value• Open-circuited heater loads• Switches stuck-at-opposite• Switches inverses intermittently• Degradation of Solar Panels

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LYDIA-NG Disambiguation

• Entropy-based methods for computing uncertainty of a component:

• Per system:

*

*

CxC

xCxCCU PrlgPr

COMPSC

CUCOMPS

U1

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Test Scenarios

ID Faults Classification Comment

TST1 No faults - Nominal Scenario

TST3One heater load (GCDE from HTRGRPA8) is open-circuited

Single Ambiguous fault -

TST4Continuation of TST3: Injection of Tests suggested by LNG

- Active testing to reduce candidates

TST7

Two heaters (GCDE & PXFA2_HPS from HTRGRPA8) open-circuited & two solar arrays (CBM-Mz & W-Mz) degraded

Abrupt Multiple Ambiguous

Simultaneously independent failures (highly unlikely case)

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Diagnoses and Metrics

• Evaluating the evaluator (comparing the performance of various diagnostic algorithms)

• Fault injection – deterministic assignment• Diagnosis

– was a set of deterministic diagnostic candidates– proposing a single probabilistic assignment

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Performance Metrics• Scenario detection accuracy, Mda

• Mfp (false positive scenario) is a scenario where diagnosis is produced but there is no injection

• Mfn (false negative scenario) is a scenario where diagnosis is not produced but there is an injection.

• Fault detection time, Mfd = FDT

• Fault Isolation time, Mfi = FDI

fpfnda MMM ,max1

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Performance Metrics (cont.)

• Classification errors

COMPSC

err xCxCIM )Pr()(

otherwise,0

)(,1)(

xCifxCI

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Performance Metrics (cont.)

• Classification errors• Uncertainty (entropy):

– the health state of each component is represented by a (random) variable

– entropy of random variable– entropy sum per system

Pr(R1 = 20) = 0.5,Pr(R1 = ∞) = 0,Pr(R1 = 1) = 0.5

Pr(R1 = 20) = 1/3,Pr(R1 = ∞) = 1/3,Pr(R1 = 1) = 1/3

Pr(R1 = 20) = 1,Pr(R1 = ∞) = 0,Pr(R1 = 1) = 0

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TST1: Nominal Scenario

– Nominal Scenario No faults injected• All heaters on demanded current of • All Solar arrays in MPPT

– Full luminosity• Main bus voltage equal to• Battery charging

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TST1: Nominal Scenario

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TST3: Single Ambiguous

– Start with nominal scenario No faults

– At some point the GCDE heater load is open-circuited

– In telemetry, value of the HTRGRPA8 LCL current sensor decreases of value

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TST3: Single Ambiguous

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TST3: Single Ambiguous

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TST3: Single Ambiguous

Fault injection:GCDE heater load OC

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TST4: Disambiguation of TST3

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TST4: Disambiguation of TST3

TST7: Multiple Ambiguous

Fault(s) injection

Command executed: GCDE_HTR Off

Command executed: IPCU_B_HTR2 Off

Command executed: EGGIF6_HTR Off

Command executed: TCVmZ_HTR Off

Command executed: TCVpZ_HTR Off

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TST7: Multiple Ambiguous

Fault(s) injectionCommand executed: GCDE_HTR Off

Command executed: IPCU_B_HTR2 OffCommand executed: EGGIF6_HTR Off

Command executed: TCVmZ_HTR OffCommand executed: TCVpZ_HTR Off

Command executed: PXFA2_HPS HTR OffCommand executed: TCVpZ_HTR ON

Command executed: TCVmZ_HTR ONCommand executed: EGGIF6_HTR ONCommand executed: IPCU_B_HTR2 ON

Command executed: GCDE_HTR ON

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GENIUS Results - I

Scenario Mfd[ms] Mfn Mfp Mda

TST1 − 0 0 1TST2 88 0 0 1TST3 88 0 0 1TST4 89 0 0 1TST5 90 0 0 1TST6 90 0 0 1TST7 93 0 0 1TST8 94 0 0 1TST9 84 0 0 1TST10 − − − −TST11 77 0 0 1

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GENIUS Results - II

Scenario Mfi[s] Merr Mia Ment

TST1 − 0 289 0TST2 − 0 289 0TST3 505 1.45 287.55 0.004TST4 − 0 289 0TST5 − 1 288 0.0035TST6 101 1 288 0.0016TST7 505 3.96 285.04 0.0047TST8 − 1 288 0.0022TST9 − 1 288 0TST10 − − − −TST11 505 1.83 287.17 0.0025

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Conclusions

• A step toward applying MBD to a real-world artifact

• Our goals:– make diagnosis universal (to a large class of real-

world systems)– develop tools

• software• publications• public awareness of the importance of diagnosis

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Future (Present) Work

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AEROCONF-2013

• Read our paper:

A. Feldman, H. V. de Castro, A. J. C. van Gemund, & G. Provan,“Model-Based Diagnostic Decision-Support System for Satellites“, in Proc. IEEE Aerospace Conference 2013 - AEROCONF'2013,pp. 1-14, 2013

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Call for Participation

• Want to play with/test/develop LYDIA-NG?– LYDIA-NG is free for academic use/open-source– send an email to alex@general-diagnostics.com

• Want to apply LYDIA-NG to your research/write a paper?

• Want to extend LYDIA-NG to solve #@! equations?

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Come to DX-2013

• Come to DX-2013, Oct 1-4, Dan Panorama Hotel, Jerusalem (http://dx-2013.org/)

• DXC-2013• synthetic track (ISCAS)• electrical system (ADAPT)• thermal fluid system (AHU-9)• software track